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2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
Towards Multi-View Object Class Detection
New York, NY
June 17-June 22
ISBN: 0-7695-2597-0
Alexander Thomas, KU Leuven (BE)
Vittorio Ferrar, INRIA Grenoble, France
Bastian Leibe, ETH Zurich (CH)
Tinne Tuytelaars, KU Leuven (BE)
Bernt Schiel, TU Darmstadt (D)
Luc Van Gool, KU Leuven (BE)
We present a novel system for generic object class detection. In contrast to most existing systems which focus on a single viewpoint or aspect, our approach can detect object instances from arbitrary viewpoints. This is achieved by combining the Implicit Shape Model for object class detection proposed by Leibe and Schiele with the multi-view specific object recognition system of Ferrari et al.

After learning single-view codebooks, these are interconnected by so-called activation links, obtained through multi-view region tracks across different training views of individual object instances. During recognition, these integrated codebooks work together to determine the location and pose of the object. Experimental results demonstrate the viability of the approach and compare it to a bank of independent single-view detectors

Citation:
Alexander Thomas, Vittorio Ferrar, Bastian Leibe, Tinne Tuytelaars, Bernt Schiel, Luc Van Gool, "Towards Multi-View Object Class Detection," cvpr, vol. 2, pp.1589-1596, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06), 2006
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